SINCE 2021 clima,software

Ready-to-use custom climate projections

CLIMADJUST

Summary

More virulent wildfires, longer droughts, extreme heat, more powerful typhoons, loss of biodiversity and worsening air quality. These are just some of the impacts of climate change that affect the entire planet, albeit differently from place to place. In order to try to mitigate and adapt to these changes, climate projections provide key insights into how climatic conditions will evolve over the coming decades.

However, the raw data provided by Global Climate Models (GCMs) or Regional Climate Models (RCMs) cannot be used directly: their resolution is usually not high enough, and certain aspects of the models are simplified (e.g. parameterisation). The result is that, while GCMs and RCMs provide a good overall picture, their outputs exhibit biases when compared with local or regional historical observations. To solve this, researchers use bias adjustment: a set of statistical techniques that allows them to use past observations to adjust future projections. This is a time- and resource-consuming process that requires technical knowledge.

Climadjust is a web service that enables users to apply bias adjustment techniques to climate projections in a simple and validated way.

Main Features

  • Upload your own climate data
  • Ready-to-use climate projections
  • Customisable
  • Easy-to-use and fast
  • Scientifically validated
CLIMADJUST CONTACT

    Customisable

    Through a user-friendly interface and a set of predefined steps, the user will be able to:

    • Upload their own datasets of observations to adjust the projections.
    • Access state-of-the-art climate datasets coming from trusted sources like Copernicus Climate Change Service or the Earth System Grid Federation.
    • Choose among six different state-of-the-art bias adjustment techniques.
      Obtain projections that are ready to be used, in standardised formats: JSON and NetCDF.
    • Validate the bias adjustment techniques on historical periods.
    • The service also provides API access, to be easily integrated into existing workflows.

    Climadjust can be tried out for free here.

    Detailed description

    Climadjust offers worldwide coverage, enabling users to upload their own data to adjust climate projections. For those cases when users don't have data of their own, Climadjust provides access to climate datasets from trusted sources, such as Copernicus Climate Data Store. In particular, it offers access to:

    • Projection datasets: climate projections from CMIP5, CMIP6 or EURO-CORDEX. Other datasets, such as other CORDEX domains, are in the process of being included.
    • Reference datasets: observational and reanalysed datasets such as ERA5, that provides hourly estimates of a large number of atmospheric, land and oceanic climate variables, covering from 1979 to within 5 days of real time.

    To adjust the data, Climadjust employs the Bias Adjustment capabilities of the Open Access framework Climate4R, continuously maintained and updated by a dedicated community of climatologists. This allows a transparent use of climate data. In particular, Climadjust offers six different Bias Adjustment techniques, including both parametric and empirical techniques, as well as trend-preserving options:

    • ISIMIP3 [1]
    • Empirical Quantile Mapping [2]
    • Detrended Quantile Matching [3]
    • Quantile Delta Mapping [4]
    • Parametric Quantile Mapping
    • Generalised Parametric Quantile Mapping [5]

    The validation techniques offered by Climadjust ensure that the whole process is transparent, providing verifiable processing information. The validation protocols were developed hand in hand with the Santander Meteorology Group at the University of Cantabria and follow the standard validation methods defined in the European VALUE COST.

    European funding & collaboration

    Climadjust was funded as a Use-Case project of the Copernicus Climate Change Service (C3S). The C3S is one of the six EU’s Copernicus Programme services implemented by the European Centre for Medium-Range Weather Forecast (ECMWF).
    In addition, Climadjust was developed in collaboration with the Spanish National Research Council (CSIC), through the Santander meteorology Group.

    Added value services

    Although Climadjust can be used as a Software as a Service (SaaS), we offer additional features, to provide a more tailored service:

    • Climate indices calculation: we can provide additional support to compute climate indices that are relevant to different sectors.
    • Custom projects: from environmental studies to risk analysis, climate projections are useful to a myriad of users. We can offer a tailored approach towards particular cases, to provide the climate data each challenge requires.
    • Training: we offer tailored training to organisations that wish to integrate the service in their workflow, to exploit the full capabilities that Climadjust offers: API access, climate expertise and much more.

    References and technical documentation

    • [1] ISIMIP3: Lange, S. (2019) . Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geoscientific Model Development , 12: 3055–3070. doi: 10.5194/gmd-12-3055-2019
    • [2]Empirical Quantile Mapping: Déqué, M. (2007) . Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statis-tical correction according to observed values. Global and Planetary Change , 57: 16 – 26, doi: 10.1016/j.gloplacha.2006.11.030
    • [3]Detrended Quantile Mapping: Cannon, A.J., S.R. Sobie and T.Q. Murdock (2015) . Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28, 6938–6959, doi: 10.1175/JCLI-D-14-00754.1
    • [4] Quantile Delta Mapping: Cannon, A.J., S.R. Sobie and T.Q. Murdock (2015) . Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28, 6938–6959, doi: 10.1175/JCLI-D-14-00754.1
    • [5] Generalised Parametric Quantile Mapping: Gutjahr, O. and Heinemann, G. (2013) . Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM, Theoretical and Applied Climatology , 114, 511-529. doi: 10.1007/s00704-013-0834-z

    Browse our projects

    We use cookies to enhance the user experience. By clicking any link on this page, you are giving your consent.